
Review of Short Phrases and Links 
This Review contains major "Confidence Interval" related terms, short phrases and links grouped together in the form of Encyclopedia article.
Definitions
 A confidence interval is an interval within which the mean of a population probably lies.
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 The confidence interval is the range of values within which the population value of the statistic is likely to fall.
 A confidence interval is a range of values that tries to quantify this uncertainty.
 The confidence interval is the length of the line between the limits.
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 A confidence interval is a range of values used to estimate the true value of a population parameter.
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 However, the confidence interval contains information about experimental precision that is not available from the result of a significance test.
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 The wider the confidence interval, the less the precision.
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 The standard error can be used to create a confidence interval within which the "true" percentage should be to a certain level of confidence.
 There are lots of different formulas for the confidence interval and the standard error, depending on the context of the problem.
 For normal distributions, the confidence interval radii are proportional to the standard error.
 The larger the sample size, the narrower the confidence interval and the more precise the estimate of risk.
 First I explain how to do it for outcome statistics whose confidence interval has a width proportional to the square root of the sample size.
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 The width of your confidence interval goes down as the sample size goes up, since you are placing a larger value in the denominator.
 The confidence interval expands to between 19.6 and 22.4, but now we are 99% confident of capturing the true population mean.
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 If you assume that your sample is randomly selected from some population, you can be 95% sure that the confidence interval includes the population mean.
 These findings result in the following confidence interval: We are 95% confident that the independent candidate will receive between 25% and 35% of the vote.
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 This will be the probability that the mean of the entire population falls within the confidence interval that you compute based on the mean of your sample.
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 By default, SPSS prints out the 95% confidence interval for ttest differences between the sample mean and the hypothesized mean.
 In other words, the maximum margin of error is the radius of a 95% confidence interval for a reported percentage of 50%.
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 If p moves away from 50 percent, the confidence interval around p will be smaller.
 The margin of error has been described as an "absolute" quantity, equal to a confidence interval radius for the statistic.
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 To conclude, the margin of error is the 99 percent confidence interval for a reported percentage of 50 percent.
 Moreover, the confidence interval is narrower than the prediction interval, which deals with individual cases.
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 Prism determines and graphs the bestfit linear regression line, optionally including a 95% confidence interval or 95% prediction interval bands.
 R2 : A DOS program for confidence interval estimation, power calculation, and sample size estimation for the squared multiple correlation.
 Confidence interval estimation provides a convenient alternative to significance testing in most situations.
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 The estimated percentage plus or minus its margin of error is a confidence interval for the percentage.
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 Note in SPSS this is referenced as the confidence interval of Exp(B), where Exp(B) is the odds ratio.
 Results of estimation can be expressed as a single value; known as a point estimate, or a range of values, referred to as a confidence interval.
 So a crude confidence interval for the log odds ratio is 0.6 plus or minus 0.9 which equals 0.5 to 1.3.
 We see that as any or all of the counts in the two by two table increase, the confidence interval for the log odds ratio shrinks.
 Twosided confidence interval for the mean and the difference between means.
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 Onesided confidence interval for the mean, proportions and difference between means.
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 In other words, the margin of error is half the width of the confidence interval.
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 In epidemiological studies, the width of the confidence interval is related to the sample size of the study.
 Most undergraduate texts in behavioral statistics show how to compute such a confidence interval.
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 In practice, a confidence interval is used to express the uncertainty in a quantity being estimated.
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 Statisticians use a confidence interval to express the precision and uncertainty associated with a particular sampling method.
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 Normally, if your confidence interval for your risk ratio contains 1 the results are considered to be statistically insignificant.
 I f the confidence interval does not overlap zero, the effect is said to be statistically significant.
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 There are numerous web sites that will calculate a binomial proportion confidence interval.
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 That is how one estimates a population proportion using a confidence interval.
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 Concepts: binomial distribution, normal distribution, central limit theorem, confidence interval.
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 In Statistics, a Binomial Proportion Confidence Interval is a confidence interval for a proportion in a statistical population.
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 Exact Confidence Interval for a Proportion uses a Bayesian interval with an uninformative prior distribution.
 A narrow confidence interval implies high precision; we can specify plausible values to within a tiny range.
 The first experiment had a very large sample size, and very high precision of measurement, reflected in a very narrow confidence interval.
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 This aids in interpreting the results, as the confidence interval for a given  simultaneously indicates both statistical significance and effect size.
 DO NOT FLY WITH STATISTICAL SIGNIFICANCE It's important to understand that you sample until you get a narrow confidence interval.
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 The second experiment clearly lacked precision, and this is reflected in the very wide confidence interval.
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 That fact and the normal and chisquare distributions given above form the basis of confidence interval calculations relying on Student's tdistribution.
 This does not mean that the confidence interval includes the possibility of curves as well as straight lines.
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 The second experiment yields a confidence interval that includes zero, so the null hypothesis of no difference is not rejected.
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 Here's an example of a confidence interval that excludes the null value.
 Compute the upper bound of the confidence interval.
 The confidence interval, in fact, contains a lower bound, but also includes an upper bound, and, in the interval width, a measure of precision of estimation.
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 The State wanted to ensure that the confidence interval did not exceed 10% of the confidence interval’s upper limit and lower limit.
 The range of the confidence interval is defined by the sample statistic + margin of error.
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 Let's look at our first confidence interval, and use it to explain more about sampling error.
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 The confidence interval indicates that the population standard deviation lies between 28.1 and 37.2.
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 Since the above requirements are satisfied, we can use the following fourstep approach to construct a confidence interval.
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 To construct a confidence interval, we need to know the variability of the difference between sample means.
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* Upper Bound

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